Optimal design and sensitivity analysis of post-combustion CO2 capture process by chemical absorption with amines
Optimal design and sensitivity analysis of post-combustion CO2 capture process by chemical absorption with amines
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DOI：
10.1016/j.jclepro.2015.12.056
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发表时间：
2016-03
影响因子：
11.1
通讯作者：
Ana M. Arias;P. Mores;N. Scenna;Sergio F. Mussati
中科院分区：
文献类型：
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作者：
Ana M. Arias;P. Mores;N. Scenna;Sergio F. Mussati
本文重点对燃烧后化学吸收co2捕集装置的具体年总成本（运行成本和投资）进行优化，并对所有模型参数进行敏感性分析。采用数学规划方法和详细的模型，对整个过程（吸收、胺再生、压缩阶段和能量回收系统）的优化设计进行了研究。为了进行更广泛的讨论，讨论了两种不同于计算总运行成本的最佳设计。在前一种情况下，运行成本包括MEA和H2O组成和冷却水成本。在第二种情况下，再沸器所需的蒸汽费用和总电费也包括在内。在这两种情况下，整个过程同时进行优化，以确定每个过程单元的最佳尺寸和使特定总成本最小化的操作条件。对两种目标函数的解进行了详细比较。然后，进行敏感性分析，以识别和研究两种最优设计如何随着模型参数的变化而修改。其中，结果表明，当参数为其标称值的±2.5%时，特定总成本变化范围为- 0.10至7.7%。然而，在几个工艺单元的最佳尺寸（根据具体情况变化高达118.30%）以及蒸汽、电力和冷却水的要求（根据具体情况变化高达27.60%）上观察到显著差异。给出了支持上述结论的数值结果，并通过若干优化解进行了讨论。
This paper focuses on the optimization of the specific total annual cost (operating costs and investments) of a post-combustion CO2capture plant with chemical absorption and on the sensitivity analyses of all of the model parameters. Using a mathematical programming approach and a detailed model the optimal design of the entire process (absorption, amine regeneration, compression stage, and energy recovery system) is investigated. For a more general discussion, two optimal designs that differ on how the total operating cost is computed are discussed. In the former case, the operating cost includes the MEA and H2O make-ups and cooling water costs. In the second case the cost of the steam required in the reboiler and the total electricity cost are also included. In both cases, the entire process is simultaneously optimized in order to determine the optimal sizes of each process unit and the operating conditions that minimize the specific total cost. The solutions obtained for both objective functions are compared in detail. Then, sensitivity analyses are performed in order to identify and to investigate how the two optimal designs are modified with the variations of the model parameters. Among others, the results revealed that the specific total cost varies from −0.10 to 7.7% when the parameters are ±2.5% of their nominal values. However, significant differences are observed in the optimal sizes in several process units (variations up to 118.30% depending on the case) and also in the requirements of steam, electricity and cooling water (variations up to 27.60% depending on the case). The numerical results that support the conclusions are presented and discussed through several optimization solutions.